The Washington Post spent a year determining how many children have been affected by school shootings, beyond just those killed or injured. To do that, reporters attempted to identify every act of gunfire at a primary or secondary school during school hours since the Columbine High massacre on April 20, 1999. Using Nexis, news articles, open-source databases, law enforcement reports, information from school websites, and calls to schools and police departments, The Post reviewed more than 1,000 alleged incidents, but counted only those that happened on campuses immediately before, during or just after classes.

Shootings at after-hours events, accidental discharges that caused no injuries to anyone other than the person handling the gun, and suicides that occurred privately or posed no threat to other children were excluded. Gunfire at colleges and universities, which affects young adults rather than kids, also was not counted.

After finding more than 200 incidents of gun violence that met The Post’s criteria, reporters organized them in a database for analysis. Because the federal government does not track school shootings, it’s possible that the database does not contain every incident that would qualify.

To calculate how many children were exposed to gunfire in each school shooting, The Post relied on enrollment figures and demographic information from the U.S. Education Department, including the Common Core of Data and the Private School Universe Survey. The analysis used attendance figures from the year of the shooting for the vast majority of the schools.

The Post’s database is updated regularly as school shootings are reported and as facts emerge about individual cases. The Post is seeking assistance in making the database as comprehensive as possible. To provide information about school shootings since Columbine that fit The Post’s definition, send us an email at schoolshootings@washpost.com.

THIS DATASET WAS LAST UPDATED AT 7:11 AM EASTERN ON DEC. 1

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

As of June 19, 116 school shooting incidents were recorded in K-12 schools in the United States in 2025. Within the provided time period, the greatest number of K-12 school shootings was recorded in 2023, at 350. The source defines a school shooting as every time a gun is brandished, fired, or a bullet hits school property for any reason, regardless of the number of victims (including zero), time, day or the week, or reason, including gang shootings, domestic violence, shootings at sports games and after hours school events, suicides, fights that escalate into shootings, and accidents.

The Rise of Home Schooling

Data from The Post's analysis of home-schooling enrollment across the US

This repository shares data hand-collected by The Washington Post from individual school districts and states as a whole regarding home-school enrollment from 2017-18 through 2022-23. The data is what is behind this story published on Oct. 31, 2023 by Peter Jamison, Laura Meckler, Prayag Gordy, Clara Ence Morse and Chris Alcantara.

There are two separate data files, both of which cover the same time period: - home_school_district.csv - home_school_state.csv

There is also a data dictionary explaining each file.

Methodology

To measure the growth of home schooling during the pandemic, The Washington Post collected home-school student counts from 6,738 school districts. Together with students from The Washington Post Investigative Reporting Workshop practicum at American University, reporters trawled state websites, contacted education officials in all 50 states and the District of Columbia and submitted multiple public records requests for an annual count of home-schoolers from the 2017-18 school year through 2022-23. The Post ultimately collected data for all public school districts in 29 states and D.C. In all, The Post gathered data from states representing 61% of the American school-age population.

Three states — Pennsylvania, Rhode Island and Tennessee — have not published the number of home-schoolers in 2022-23, and Maine only shared district-level data starting with the 2020-21 school year. In seven states, The Post was unable to obtain usable home-school enrollment figures: In Arizona, Nevada and Oregon, only new home-school registrations are tracked annually at the district level; in North Carolina, home-school registration rolls are not regularly purged as students age out of the system; and in West Virginia, Utah and Alabama, annual enrollment data is unavailable. Eleven additional states do not require any notice when families decide to home-school their children, so enrollment figures in those states are also unavailable. Finally, Montana, Vermont and Nebraska collect data at a county level, not a district level, so there is no district data available - only statewide figures.

The Post made every effort to capture all legal ways to home-school, which vary by state. However, data on home schools established by certain methods, such as registering one’s home-school as a private school, are tracked by some states but not others. That means The Post’s tally is almost certainly an undercount, even in the states from which it gathered data. For instance, Wisconsin and Georgia only provided The Post with tallies of home-schoolers who had submitted required forms electronically. In Kentucky, some districts incorrectly reported zero or one home-schooled students in certain years, which a state education official attributed to an unclear form. The Post excluded those enrollment figures from its analysis. In California, which does not explicitly permit home schooling, many parents operate home-based private schools. The California Department of Education characterizes private schools with five or fewer students as home schools. In Louisiana, many home schools operate as nonpublic schools not seeking accreditation; The Post counted such schools with five or fewer students as home schools as well.

The statewide numbers are not always equivalent to the sum of all district totals in a state. Some states suppress district-level counts of home schoolers below a certain threshold. In Maine, the threshold is 5; in New Mexico, 6; in Mississippi, Ohio and Tennessee, 10; in Wisconsin before 2020-21, 5; and in Wisconsin from 2021-22 on, 20. The Post marked such suppressions as NA within its data. In addition, New Hampshire collects separate data on students who enter home schooling from schools run by the state department of education or from private schools; these additional students are reflected in state data but not district data.

The Post used a variety of methods to match each school district name to an NCES district id. However, this was not always possible. In Georgia, families self-report their school district on home-schooling forms; some report programs which are not school districts, and therefore have no corresponding NCES id. In California, families were only required to report county and school district beginning in 2020-21; in addition, district mergers and name changes mean that some districts could not be matched wi...

This dataset contains details of all preferences in past junior admissions rounds - that is when applications are received for York children starting junior school in Year 3 for the first time. It includes all preferences by preference status (allocated, refused, withdrawn) and by admissions criteria group (catchment, religion, sibling etc). For further information and advice on school admissions you may wish to consult the Guide for Parents or School Admissions at CYC's webpage

This dataset contains the number of students attending different types of Massachusetts schools by town since 1985. It includes full-time students whose parents or legal guardians are residents of the city or town.

This report is required from each city and town under the provisions of Chapter 72, Section 2A of the General Laws. The information is as of January 1st of the given school year. A value of zero indicates that the city or town does not have any students enrolled in the specified type of school.

This dataset contains the same data that is also published on our DESE Profiles site: School Attending Children Report

List of School Types

• Local Public Schools
• Academic Regional Schools
• Vocational Technical Regional Schools
• Collaboratives
• Charter Schools (included since 2011)
• Out-of-District Public Schools
• Home Schooled (included since 2011)
• In State Private and Parochial Schools
• Out-of-State Private and Parochial Schools

This dataset contains details of all preferences in past secondary admissions rounds - that is when applications are received for York children starting secondary school in Year 7 for the first time. It includes all preferences by preference status (allocated, refused, withdrawn) and by admissions criteria group (catchment, religion, sibling etc). For further information and advice on school admissions you may wish to consult the Guide for Parents or School Admissions at CYC's webpage

This web map provides and in-depth look at school districts within the United States. Clicking on a school district in the map will reveal different statistics about each district in the pop-up. The statistics presented in this map are approximations based on summarizing American Community Survey(ACS) data using tract centroids. They may differ from published statistics by school districts found on data.census.gov. A few things you will learn from this map:How many public and private schools fall within a district?Socioeconomic factors about the Census Tracts which fall within the district:School enrollment for grades Kindergarten through 12thDisconnected children in the districtChildren living below the poverty level Children with no internet at home Children without a working parentRace/ethnicity breakdown of population under the age of 19 in the districtFor more information about the data sources:This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases estimates, so values in the map always reflect the newest data available.Current School Districts Layer:The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are single-purpose administrative units designed by state and local officials to organize and provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to support educational research and program administration, and the boundaries are essential for constructing district-level estimates of the number of children in poverty.The Census Bureau’s School District Boundary Review program (SDRP) (https://www.census.gov/programs-surveys/sdrp.html) obtains the boundaries, names, and grade ranges from state officials, and integrates these updates into Census TIGER. Census TIGER boundaries include legal maritime buffers for coastal areas by default, but the NCES composite file removes these buffers to facilitate broader use and cleaner cartographic representation. The NCES EDGE program collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop the composite school district files. The inputs for this data layer were developed from Census TIGER/Line and represent the most current boundaries available. For more information about NCES school district boundary data, see https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries.Public Schools Layer:This Public Schools feature dataset is composed of all Public elementary and secondary education facilities in the United States as defined by the Common Core of Data (CCD, https://nces.ed.gov/ccd/ ), National Center for Education Statistics (NCES, https://nces.ed.gov ), US Department of Education for the 2017-2018 school year. This includes all Kindergarten through 12th grade schools as tracked by the Common Core of Data. Included in this dataset are military schools in US territories and referenced in the city field with an APO or FPO address. DOD schools represented in the NCES data that are outside of the United States or US territories have been omitted. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 3065 new records, modifications to the spatial location and/or attribution of 99,287 records, and removal of 2996 records not present in the NCES CCD data.Private Schools Layer:This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.Web Map originally owned by Summers Cleary

Dataset Overview 📝

The dataset includes the following key indicators, collected for over 200 countries:

• Government Expenditure on Education (% of GDP): Shows the percentage of a country’s GDP allocated to education.
• Literacy Rate (Adult Total): Represents the percentage of the population aged 15 and above who can read and write.
• Primary Completion Rate: The percentage of children who complete their primary education within the official age group.
• Pupil-Teacher Ratio (Primary and Secondary Education): Indicates the average number of students per teacher at the primary and secondary levels.
• School Enrollment Rates (Primary, Secondary, Tertiary): Reflects the percentage of the relevant age group enrolled in schools across different education levels.

Data Source 🌐

World Bank: This dataset is compiled from the World Bank's educational database, providing reliable, updated statistics on educational progress worldwide.

Potential Use Cases 🔍 This dataset is ideal for anyone interested in:

Educational Research: Understanding how education spending and policies impact literacy, enrollment, and overall educational outcomes. Predictive Modeling: Building models to predict educational success factors, such as completion rates and literacy. Global Education Analysis: Analyzing trends in global education systems and how different countries allocate resources to education. Policy Development: Helping governments and organizations make data-driven decisions regarding educational reforms and funding.

Key Questions You Can Explore 🤔

How does government expenditure on education correlate with literacy rates and school enrollment across different regions? What are the trends in pupil-teacher ratios over time, and how do they affect educational outcomes? How do education indicators differ between low-income and high-income countries? Can we predict which countries will achieve universal primary education based on current trends?

Important Notes ⚠️ - Missing Data: Some values may be missing for certain years or countries. Consider using techniques like forward filling or interpolation when working with time series models. - Data Limitations: This dataset provides global averages and may not capture regional disparities within countries.

Percentage of school children who spend a minimum of two hours each week on high quality PE and school sport within and beyond the curriculum. There is the potential for error in the collection, collation and interpretation of the data (bias may be introduced due to poor response rates and selective opt out of larger children which it is not possible to control for). The percentage of children attending state schools belonging to a School Sport Partnership who participate in at least 2 hours of high quality PE and school sport in a typical week of the academic year. TNS Social Research: Annual Survey of School Sport Partnerships on behalf of the Department for Children, Schools and Families. Visit the APHO website

MD: Children Out of School: % of Primary School Age data was reported at 10.050 % in 2015. This records an increase from the previous number of 9.998 % for 2014. MD: Children Out of School: % of Primary School Age data is updated yearly, averaging 9.421 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 10.050 % in 2015 and a record low of 3.361 % in 2003. MD: Children Out of School: % of Primary School Age data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank: Education Statistics. Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

This dataset contains details of all applicants in past junior admissions rounds - that is when applications are received for York children starting junior school in Year 3 for the first time. It includes all preferences, allocated school and the catchment school for each application. For further information and advice on school admissions you may wish to consult the Guide for Parents or School Admissions at CYC's webpage

This dataset contains details of all applicants in past secondary admissions rounds - that is when applications are received for York children starting secondary school in Year 7 for the first time. It includes all preferences, allocated school and the catchment school for each application. For further information and advice on school admissions you may wish to consult the Guide for Parents or School Admissions at CYC's webpage

This data tracks the deaths of children up to 18 years old and whether or not the child, youth or their family were involved with a children's aid society within 12 months of their death. This data is provided to the Office of the Chief Coroner by the Registrar General of Ontario and by children's aid societies and has not been independently verified by the Office of the Chief Coroner.

In 1990, the United States Congress authorized a major program designed to enhance the early public school transitions of former Head Start children and their families. Former Head Start children, like many other children living in poverty, were at risk for poor school achievement. This new program was launched to test the value of extending comprehensive, Head Start-like supports "upward" through the first four years of elementary school. This project, administered by the Head Start Bureau of the Administration on Children, Youth, and Families, funded 31 local Transition Demonstration Programs in 30 states and the Navajo Nation from the 1991-1992 school year through the 1997-1998 school year and involved more than 450 public schools. The National Transition Demonstration Study was conducted to provide information about the implementation of this program and its impact on children, families, schools, and communities. More than 7,500 former Head Start children and families were enrolled in the National Study. Thousands of other children and families, however, participated in the Transition Demonstration Program, since supports and educational enhancements were offered to all children and families in the classrooms. The datasets are organized into four broad categories: Family Units -- There are six family unit files. A "family unit" record consists of information about a child or family as the result of source data taken from family interviews, records of child test scores on a child instrument, school archival records, or teacher questionnaires (Part B). If data were available from any combination of these source documents, a family unit record was generated. A broad range of variables are included under this heading. Variables range from simple demographics to standardized scores of social skill ratings as well as neighborhood factor scores and child outcome scores in reading and mathematics. These files are associated with each year of the child's schooling (kindergarten through third grade). School Unit -- There are five school unit files, organized around the year of data collection. A "school unit" record consists of information about a school as the result of source data captured from family interviews, a classroom teacher, or the school principal. The structure of this data file is different from others in that rather than being merged on a common key, the records are actually stacked one upon the other in groups. The first part of the file consists of family data, the middle portion consists of teacher data, and the final portion consists of principal data. A key variable to the construction of this dataset is the REC_SRC (record source) variable. It informs the user as to the source of the data in the record. The abbreviations are "fi," "ta," and "qp" for family interview, teacher questionnaire (Part A), and questionnaire for principals, respectively. The data viewed as the centerpiece of these datasets are the school climate survey variables and their associated factor scores. Classroom Unit -- There are five classroom unit files organized around the year of data collection. The data recorded focus on the classroom and are from the following sources: classroom composition, assessment profile, a developmentally appropriate practice template, and a teacher questionnaire (part a). Some of the data available address the social skills the teacher views as important to his or her particular classroom. Variables addressing diversity of both gender and ethnicity within a single classroom are included when available. Exit Interviews -- There are five Exit Interviews. Exit information was collected from the following groups: experimental and control families, family service specialists, school principals, and classroom teachers. These exit interviews were conducted upon exit from the third grade, and have been combined for both cohorts. Community Characteristics Data -- The community characteristics dataset is a hierarchical file having four distinct levels of data. The type of information available in this file may include data that describe the site, county, school district, or study school. Users of these data are strongly encouraged to consult the accompanying documentation before attempting to use these files.

Context

Teenagers tend to have confusion in deciding which Field/Hobby they fit in or love to do, so we attempted in collecting data from parents as a survey and then converting it into an appropriate tabular form, to prepare a classification model.

Content

This dataset contains 13 attributes and 3 labels, having Categorical, Boolean, and Numerical data. | Features | Meaning | | --- | --- | | Olympiad_Participation | Has your child participated in any Science/Maths Olympiad? | | Scholarship | Has he/she received any scholarship?| | School | Love's going to school? | | Fav_sub | What is his/her favorite subject? | | Projects | Has done any projects under academics before? | | Grasp_pow | His/Her Grasping power (1-6) | | Time_sprt | How much time does he/she spend playing outdoor/indoor games? | | Medals | Medals won in Sports? | | Career_sprt | Want's to pursue his/her career in sports? | | Act_sprt | Regular in his/her sports activities? | | Fant_arts | Love creating fantasy paintings? | | Won_arts | Won art competitions? | | Time_art | Time utilized in Arts? |

Inspiration

One can build a recommendation system using this dataset.

[If you find it useful, please give an upvote !]

Kaggle Dataset Description

Title: School Enrollment, Primary (% Net)
Subtitle: Exploring global trends in access to primary education.

Detailed Description:
This dataset contains data on net primary school enrollment rates, sourced from the World Bank. It measures the proportion of children enrolled in primary education who belong to the official age group for that level, expressed as a percentage of the total population of that age group.

Key Highlights: - Annual data for countries worldwide.
- Metric: Net primary school enrollment (%).
- Use cases: Analyze trends, compare regional disparities, and study relationships with socio-economic factors like GDP, literacy, and gender equality.

4. Exploratory Data Analysis (EDA)

Notebook Ideas

1. Data Cleaning:

   • Handle missing or inconsistent data points.
   • Normalize data for comparison across regions.
   • Aggregate data by regions (e.g., high-income vs. low-income countries).

2. Visualizations:

   • Line Graph: Trends in net enrollment rates over time for selected countries.
   • Heatmap: Net enrollment rates by region and year.
   • Scatterplot: Correlation between net enrollment and GDP, literacy rates, or gender equality.
   • Bar Chart: Top and bottom countries by net enrollment for a specific year.

3. Descriptive Analysis:

   • Highlight regions with near-universal enrollment.
   • Identify countries with significant improvements or declines in enrollment rates.
   • Analyze trends in gender disparities (if available).

5. Predictive Analysis (Optional)

• Use time-series forecasting (e.g., ARIMA or Prophet) to predict future enrollment rates for specific regions or countries.
• Apply clustering algorithms to group countries with similar educational trends.

6. Kaggle Notebook

Create a Kaggle notebook with:
1. Data Cleaning: Show how missing or inconsistent values are handled.
2. EDA: Include visualizations like heatmaps, scatterplots, and line graphs.
3. Insights: Highlight findings such as regions with the highest net enrollment or disparities over time.
4. Optional Predictive Modeling: Use forecasting models to predict future enrollment trends.

7. Call to Action

For GitHub:

• Share the GitHub repository link on LinkedIn, Twitter, and relevant forums.
• Invite collaboration:
  • "Fork this repository and contribute by adding insights, analyses, or visualizations!"

GitHub Link: https://github.com/AmsalAli/Primary_School_Enrollment_Trends

For Kaggle:

• Encourage upvotes:
  • "If this dataset is helpful, please upvote to make it more visible to the Kaggle community!"
• Engage users with questions:
  • "Which countries have achieved universal primary school enrollment?"
  • "How does GDP or literacy impact primary school enrollment rates?"

Kaggle Link: https://www.kaggle.com/datasets/yourusername/primary-school-enrollment

8. LinkedIn Post

Post Title:
📚 Global Trends in Primary School Enrollment 🌍

Post Body:
Excited to share my latest dataset on net primary school enrollment rates, sourced from the World Bank. This dataset measures the proportion of children enrolled in primary education who belong to the official age group for that level, offering key insights into education access globally.

📂 Explore the Dataset:
- GitHub Repository: https://github.com/AmsalAli/Primary_School_Enrollment_Trends
- Kaggle Dataset: https://www.kaggle.com/datasets/yourusername/primary-school-enrollment

Why It Matters:

Education is fundamental to global development. This dataset is ideal for:
- Trend Analysis: Analyze primary school enrollment across countries and regions.
- Regional Comparisons: Explore disparities in education access.
- Correlations: Study relationships between enrollment rates, GDP, gender equality, and literacy.

📈 Get Involved:
- Use this dataset for analysis and visualizations.
- Share your insights and findings.
- Upvote on Kaggle if you find it useful to help others discover it!

❓ What trends or correlations do you see?
- Which countries have achieved near-universal primary school enrollment?
- What factors drive improvements in education access?

Let me know your thoughts, and feel free to share this resource with your network! 🌟

DataScience #Education #SchoolEnrollment #GlobalD...

Context Research shows that pre-primary education is critical for setting strong foundations for a child’s social, emotional and overall well-being. The early years of a child’s life build the basis for lifelong growth and children who fall behind in these early years often never catch up with their peers, leaving these children more vulnerable to underachievement and dropping out of school.

Access to pre-primary education has almost doubled over the past 20 years. Despite this progress, more than 175 million children – or half of the pre-primary aged children globally – do not have access to this important experience. On a global scale, gender disparity is low, but wealth and location have a big influence on access. This means that both, boys and girls have roughly equal access, but children from rural areas, families with low socioeconomic status and less-developed regions experience higher barriers to an appropriate education. In sub-Saharan Africa, in particular, access to pre-primary education for children from the poorest families is less than half as compared to children from the wealthiest families.

Content this dataset content the pre-primary education rate more than 200 countries.

Acknowledgements Thanks for UNICEF for sharing data.

Inspiration This dataset provides complete information about child mortality rate. There are many inferences that can be made from this dataset. There are a few things I would like to understand from this dataset.

1. Various factors affecting the pre-primary education rate.
2. What are all the factors that make the difference between rich and poor countries?
3. What could be done to decrease the pre-primary education rate in poor countries?

The Influencing Travel Behaviour Team (ITB) provide road safety education, training and publicity to schools, communities, businesses and Leeds residents. We promote sustainable travel throughout Leeds along with helping schools and businesses to develop and implement their travel plans (which promote safe, sustainable and less car dependent patterns of travel). Each year we request mode of travel data from schools in Leeds via a SIMS report or excel spreadsheet. The 10 modes of travel specified in the data collection are: Bus (type not known), Car Share (children travelling together from different households), Car/Van, Cycle, Dedicated School Bus, Other, Public Bus Service, Taxi, Train, Walk (including scooting) This collection forms part of the Statutory duty local authorities have to monitor the success of promoting sustainable travel, and in some cases is linked to a school’s planning obligated travel plan. It is an important part of improving road safety and promoting healthy lifestyles among children in Leeds but since the council declared a climate emergency in March of this year the data is even more valuable. The data helps us understand the environmental context in Leeds and work to effectively limit carbon emissions wherever possible. We strongly encourage all schools to provide the data but not all of them respond to the request and we do not always receive a response for every pupil/student so some school response rates may be low.

BackgroundIn the US, people who don’t speak English well often have a lower quality of life than those who do [1]. They may also have limited access to health care, including mental health services, and may not be able to take part in key national health surveys like the Behavioral Risk Factor Surveillance System (BRFSS). Communities where many people have limited English skills tend to live closer to toxic chemicals. Limited English skills can also make it harder for community members to get involved in local decision-making, which can affect environmental policies and lead to health inequalities. Data SourceWashington Office of the Superintendent of Public Instruction (OSPI) | Public Records CenterMethodologyThe data was collected through a public records request from the OSPI data portal. It shows what languages students speak at home, organized by school district. OSPI collects and reports data by academic year. For example, the 2023 data comes from the 2022-2023 school year (August 1, 2022 to May 31, 2023). OSPI updates this information regularly.CaveatsThese figures only include households with children enrolled in public schools from pre-K through 12th grade. The data may change over time as new information becomes available. Source1. Shariff-Marco, S., Gee, G. C., Breen, N., Willis, G., Reeve, B. B., Grant, D., Ponce, N. A., Krieger, N., Landrine, H., Williams, D. R., Alegria, M., Mays, V. M., Johnson, T. P., & Brown, E. R. (2009). A mixed-methods approach to developing a self-reported racial/ethnic discrimination measure for use in multiethnic health surveys. Ethnicity & disease, 19(4), 447–453.CitationWashington Tracking Network, Washington State Department of Health. Languages Spoken at Home. Data from the Washington Office of Superintendent of Public Instruction (OSPI). Published January 2026. Web.